Asymmetric foraging lowers the trophic level and omnivory in natural food webs
Bibliographic record
Abstract
Food webs capture the trophic relationships and energy fluxes between species, which has fundamental impacts on ecosystem functioning and stability. Within a food web, the energy flux distribution between a predator and its prey species is shaped by food quantity-quality trade-offs and the contiguity of foraging. But the distribution of energy fluxes among prey species as well as its drivers and implications remain unclear. Here we used 157 aquatic food webs, which contain explicit energy flux information, to examine whether a predator's foraging is asymmetric and biased towards lower or higher trophic levels, and how these patterns may change with trophic level. We also evaluate how traditional topology-based approaches may over- or under-estimate a predator's trophic level and omnivory by ignoring the asymmetric foraging patterns. Our results demonstrated the prevalence of asymmetric foraging in natural aquatic food webs. Although predators prefer prey at higher trophic levels with potentially higher food quality, they obtain their energy mostly from lower trophic levels with a higher food quantity. Both tendencies, that is, stronger feeding preference for prey at higher trophic levels and stronger energetic reliance on prey at lower trophic levels are alleviated for predators at higher trophic levels. The asymmetric foraging lowers trophic levels and omnivory at both species and food web levels, compared to estimates from traditional topology-based approaches. Such overestimations by topology-based approaches are most pronounced for predators at lower trophic levels and communities with higher number of trophic species. Our study highlights the importance of energy flux information in understanding the foraging behaviour of predators as well as the structural complexity of natural food webs. The increasing availability of flux-based food web data will thus provide new opportunities to reconcile food web structure, functioning and stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".